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Dr Sibendu Samanta, Assistant Professor in the Department of Electronics and Communication Engineering, and Ms Radha Abburi, a PhD Scholar, have made significant strides in the field of fetal health monitoring. Their paper, titled “Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography,” has been published in the prestigious Q1 Journal, Applied Soft Computing, which boasts an impressive impact factor of 7.2.

This pioneering study addresses the critical gaps in the analysis of Fetal Heart Rate (FHR) recordings by leveraging wearable Phonocardiography (PCG) signals and advanced AI algorithms. The primary goal of the research is to achieve accurate classification results through the remote monitoring of fetal heartbeats. Additionally, the study tackles complex issues related to data quantity and the inherent complexity of FHR analysis. Dr Samanta and Ms Abburi’s work represents a significant advancement in the field, promising to enhance the accuracy and reliability of fetal health monitoring, ultimately contributing to better prenatal care.

Abstract of the Research:

Fetal phonocardiography (FPCG) is a non-invasive Fetal Heart Rate (FHR) monitoring technique that can detect vibrations and murmurs in heart sounds. However, acquiring fetal heart sounds from a wearable FPCG device is challenging due to noise and artefacts. This research contributes a resilient solution to overcome the conventional issues by adopting Artificial Intelligence (AI) with FPCG for automated FHR monitoring in an end-to-end manner, named (AI-FHR). Four sequential methodologies were used to ensure reliable and accurate FHR monitoring. The proposed method removes low-frequency noises and high-frequency noises by using Chebyshev II high-pass filters and Enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ECEEMDAN) in combination with Phase Shifted Maximal Overlap Discrete Wavelet Transform (PS-MODWT) filters, respectively.

The denoised signals are segmented to reduce complexity, and the segmentation is performed using multi-agent deep Q-learning (MA-DQL). The segmented signal is provided to reduce the redundancies in cardiac cycles using the Artificial Hummingbird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm called variational auto-encoder-general adversarial networks (VAE-GAN). The feature extraction and classification are carried out by adopting a hybrid of the bidirectional gated recurrent unit (BiGRU) and the multi-boosted capsule network (MBCapsNet). The proposed method was implemented and simulated using MATLAB R2020a and validated by adopting effective validation metrics.

The results demonstrate that the proposed method performed better than the current method with accuracy (81.34%), sensitivity (72%), F1-score (83%), Energy (0.808 J), and complexity index (13.34). Like other optimization methods, AHO needs precise parameter adjustment in order to function well. Its performance may be greatly impacted by the selection of parameters, including population size, exploration rate, and learning rate.

The title of the Research Paper in the Citation Format:
R. Abburi, I. Hatai, R. Jaros, R. Martinek, T. A. Babu, S. A. Babu, S. Samanta, “Adopting artificial intelligence algorithms for remote fetal heart rate monitoring and classification using wearable fetal phonocardiography”, Applied Soft Computing, vol. 165, pp. 112049, 2024, ISSN 1568-4946.

Practical Implementation or the Social Implications Associated with the Research

• Chebyshev filter and EC2EMDAN-PS-MODWT reduce low and high frequency noises.
• MA-DRL and optimization algorithms reduce complexity during classification.
• Machine learning spectrogram conversion to capture time, frequency, and spectral variations.
• Hybrid deep learning algorithms can be used to reduce positive rates.

Collaborations:

• Dr. Indranil Hatai (Signal Processing and FPGA, Mathworks, Bangalore, India)
• Dr. T. Arun Babu (HoD, Dept. of Pediatrics, All India Institute of Medical Sciences (AIIMS), Andhra Pradesh, India)
• Dr. Sharmila Arun Babu, MBBS, MS (HoD, Dept. of Obstetrics and Gynecology, All India Institute of Medical Sciences (AIIMS), Andhra Pradesh, India)
• Dr. Rene Jaros (Dept. of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, 708 00, Ostrava, Czechia)
• Prof. Radek Martinek (Dept. of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB–Technical University of Ostrava, 708 00, Ostrava, Czechia)

Future Research Plans:

• Design a low cost for continuous fetal heart rate (FHR) monitoring system
• Develop a proper deep learning algorithm to get a proper understanding of fetal’s abnormality.

Link to the article

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The Department of Economics proudly announces that Mr Nilesh A, a third-year B.Sc. Economics (Hons.) student, has secured a highly coveted one-month research-based internship at the Reserve Bank of India, Mumbai. This internship is under the Department of Economic and Policy Research (DEPR).

“I am thrilled to share my experience of securing an internship at the Reserve Bank of India, a journey that was significantly supported by the resources and guidance provided by my university. I am grateful to all my professors at Easwari School of Liberal Arts at SRM University-AP for their unwavering support and encouragement. This internship is a pivotal step in my career, and I am excited about the future and eager to continue building on this incredible foundation,” stated Nilesh while expressing his gratitude for this once-in-a-lifetime opportunity.

Internships are remarkable opportunities to gain experience and exposure, build a strong network, and hone the skills you already possess. The Easwari School of Liberal Arts of SRM University-AP provides academic and research internships prioritising experiential and industry-based learning to help students cultivate a refined practical skillset.

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The Department of Electronics and Communication Engineering is delighted to announce that Assistant Professor Dr Anirban Ghosh, PhD scholars Mr Naga Srinivasarao and Ms Manasa Santhi, and BTech student Mr Sk Abdul Hakeem have filed and published their patent, “A System and a Method for Low Transmission Delay and Energy Efficiency,” with Application No: 202441045389. The research cohort has demonstrated groundbreaking research on integrating Small-World Characteristics (SWC) into Low-Power Wide-Area Networks (LPWANs) through Reinforcement Learning.

Abstract

To support the rapid growth of Internet of Things (IoT) applications, networking technologies like Low-Power Wide-Area Networks (LPWANs) are evolving to provide extended network lifespan and broader coverage for Internet of Things Devices (IoDs). These technologies are highly effective when devices remain stationary under static conditions. However, practical IoT applications, ranging from smart cities to mobile health monitoring systems, involve heterogeneous IoDs that move dynamically, leading to changing network topologies. Typically, dynamic networks use multi-hop data transmission schemes for communication, but this method presents challenges such as increased data latency and energy imbalances. To address these issues, this patent introduces a novel approach that integrates recent advancements in social networks, specifically Small-World Characteristics (SWC), into LPWANs using Reinforcement Learning. Specifically, the SWCs are embedded into heterogeneous LPWANs through the Q-learning technique. The performance of the developed heterogeneous Small-World LPWANs is then evaluated in terms of energy efficiency (including the number of alive and dead IoDs, as well as network residual energy) and data transmission delay within the network.

Explanation of Research in Layperson’s Terms

The existing or the present technology moves around the applications that are either static or dynamic in nature, but the current invention considers a realistic IoT application that contains both static and dynamic nodes in the network. However, maintaining low data transmission delay and high network longevity over such a heterogeneous network is a challenge. By integrating SWCs over the developed heterogeneous networks using Q-learning technique helps in minimizing the data transmission delay and improves the network lifetime (energy efficient data transmission).

Practical Implementation of the Research

Applications that contain both static and dynamic nodes, such as smart health care systems, smart environmental monitoring systems, real-time traffic monitoring systems, and smart cities and homes, require less data transmission delay and high network longevity.

Collaborations

1. Dr Om Jee Pandey – Assistant Professor, Department of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi
2. Dr Satish Kumar Tiwari – Assistant Professor, IIITDM Jabalpur, India

In the next phase of research, the reserach team will work towards investigating how the energy efficiency and other quality of service of smart devices in an IoT setting can be improved if they are completely mobile.

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The Department of Chemistry is glad to announce that Dr Rajapandiyan Panneerselvam, Associate Professor, Ms Jayasree K, Research Scholar, and Ms Mounika Renduchintala, BSc student, have had their breakthrough research published as a patent titled “A Method for Detecting Microplastics from Contaminated Products” with Application Number: 202441045388. Various research has been undertaken by scientists in developing improved methods for sample preparation and data analysis, aiming to reliably detect pollutants like microplastics in complex samples such as sea salt, soil, and water. In line with these efforts, this patent introduces a rapid and easy method to detect microplastics in contaminated products and water bodies using a filter paper-based substrate.

Abstract

Surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most promising analytical tools in recent years due to its advantageous features, such as high sensitivity, specificity, ease of operation, and rapid analysis. These attributes make SERS particularly well-suited for environmental and food analysis. However, detecting target analytes in real samples using SERS faces several challenges, including matrix interference, low analyte concentrations, sample preparation complexity, and reproducibility issues. Additionally, the chemical complexity of pollutants and environmental factors can impact SERS measurements. Overcoming these hurdles demands optimized experimental conditions, refined sample preparation methods, and advanced data analysis techniques, often necessitating interdisciplinary collaborations for effective analysis. Therefore, our focus lies in the development of various methods for fabricating SERS substrates, pretreating analytes, and devising sample preparation strategies. These efforts aim to enable the detection of analytes like microplastics within complex real samples, including sea salts, soil samples, lake water, and various food products.

Practical Implementation/ Social Implications of the Research

SERS Community: Introducing a facile fabrication method for developing filter paper-based substrates, utilizing evaporation-induced self-assembly methods with the aid of 96-well plates. These substrates boast exceptional sensitivity and uniformity, exhibiting a relative standard deviation (RSD) of 8.2%. They offer easy fabrication and serve as effective SERS substrates for various applications.

Industry and Government Bodies: This invention plays a pivotal role in assessing contamination in food and water bodies, serving as a crucial tool in monitoring environmental contamination through on-site analysis with portable instruments. It ensures adherence to regulatory standards and safeguards public health.

Research: Beyond its practical applications, the invention supports scientific research endeavors focused on identifying microplastic contaminants in real-world samples using portable Raman spectrometers. This not only aids ongoing research but also paves the way for future studies in this critical field.

Collaborations

• Dr Hemanth Noothalapati – Raman Project Center for Medical and Biological Applications, Shimane University, Japan
• Dr Murali Krishna C – Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai, India
• Dr Soma Venugopal – University of Hyderabad, India

The research team hopes to develop a novel SERS substrate for the detection of environmental pollutants in real-world samples.

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Dr Vineeth Thomas, Assistant Professor in the Department of Political Science, has recently published a paper titled “Policy Responses to Caste Violence in Tamil Nadu” in the esteemed journal Economic and Political Weekly (EPW). In the paper, Dr. Thomas offers valuable insights into the crucial issue of caste violence in Tamil Nadu and examines various policy responses aimed at addressing this complex societal challenge.
Dr Thomas’s research illuminates the dynamics of caste violence and provides a comprehensive analysis of the policy measures adopted to tackle this pressing issue. His work in the EPW is expected to significantly contribute to the discourse on caste-based conflicts and policy formulation in the region. The publication of this paper not only exemplifies Dr. Vineeth Thomas’s scholarly prowess but also underscores SRM University—AP’s commitment to fostering impactful research in the realm of social and political sciences. It is anticipated that this publication will stimulate further academic dialogue and influence policy considerations in the domain of caste relations and violence in Tamil Nadu.

Abstract of the Research
This study examines the policy response to caste violence in schools in Tamil Nadu, particularly through the recommendations of a committee led by retired Justice K. Chandru. The committee’s report highlights pervasive caste discrimination in schools and proposes various measures, including teacher transfers, banning caste markers, and implementing orientation programs on caste-related issues. The report also suggests the establishment of School Welfare Officers and Social Justice Student Forces, along with a robust grievance redressal mechanism. Despite opposition and criticism, these recommendations represent a significant step toward addressing caste discrimination in Tamil Nadu’s educational institutions.

Research in Layperson’s Terms

This research focuses on the problem of caste discrimination in schools in Tamil Nadu, India. Despite the state’s reputation for promoting social justice, caste-based violence still occurs, even among students. A committee led by retired Justice K. Chandru made several recommendations to address this issue, such as banning caste markers like wristbands and educating students and teachers about discrimination. The report also suggests having specific officers to ensure these measures are followed. While these recommendations aim to create a fairer school environment, their success depends on proper implementation and support from the community.

Practical Implementation and the Social Implications Associated

Implementing this research can lead to more inclusive and equitable school environments by eliminating caste-based discrimination. By enforcing bans on caste markers, educating students and teachers, and establishing grievance mechanisms, schools can foster a culture of equality, reducing social tensions and promoting a just society for future generations.

Collaborations

Electoral Politics

Future Research Plans

Indian govt and politics

Link to the Article

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Dr Rajapandiyan P, Associate Professor, Department of Chemistry, and his PhD scholar, Ms Arunima Jinachandran, recently filed and published a patent, “A Substrate for Contaminant Detection and a Process for its Synthesis,” with Application Number: 202441043642 in the Patent Office Journal. The research duo has developed a novel SERS (Surface-Enhanced Raman Spectroscopy) substrate by synthesising silver nanopopcorn and depositing it on a polycarbonate membrane.

This novel substrate demonstrates excellent uniformity, reproducibility, and mechanical stability. It is used for the sensitive detection of toxic antibiotic nitrofurazone on fish surfaces and in honey. This breakthrough could significantly enhance food safety monitoring by providing a reliable and efficient method for detecting harmful substances.

Abstract

Detecting nitrofurazone (NFZ) in aquaculture and livestock is crucial due to its carcinogenic properties. This study presents a flexible polycarbonate membrane (PCM) with three-dimensional silver nanopopcorns (Ag NPCs) for NFZ detection on fish surfaces using surface-enhanced Raman spectroscopy (SERS). The Ag-NPCs/PCM substrate demonstrates a significant Raman signal enhancement (EF = 2.36 × 106) due to hotspots from nanoscale protrusions and crevices. It achieves a low limit of detection (LOD) of 3.7 × 10−9 M, with uniform and reproducible signals (RSD < 8.34%) and retains 70% efficacy after 10 days. The practical detection LODs for NFZ in tap water, honey water, and on fish surfaces are 1.35 × 10−8 M, 5.76 × 10−7 M, and 3.61 × 10−8 M, respectively, demonstrating its effectiveness for various samples. This Ag-NPCs/PCM substrate offers a promising approach for sensitive SERS detection of toxic substances in real-world applications.

Practical Implementation/ Social Implications of the Research

The practical applicability of the proposed Ag-NPCs/PCM SERS substrate is validated by successfully detecting NFZ in various actual samples, such as tap water, honey water, and irregular fish surfaces.

Collaborations – Prof. Tzyy-Jiann Wang – National Taipei University of Technology, Taiwan

Dr Rajapandiyan and Ms Arunima will continue to work towards the development of novel flexible SERS substrates for detecting toxic pollutants in food.

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Seema Ratnam

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Andhra Patrika

Andhra Prabha

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